Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirements into correct optimization formulations and solver-executable code remains labor-intensive. Although large language models (LLMs) have been explored for this task, evaluation is still dominated by toy-sized or synthetic benchmarks, masking the difficulty of industrial problems with $10^{3}$--$10^{6}$ (or more) variables and constraints. A key bottleneck is the lack of benchmarks that align natural-language specifications with reference formulations/solver code grounded in real optimization models. To fill in this gap, we introduce MIPLIB-NL, built via a structure-aware reverse construction methodology from real mixed-integer linear programs in MIPLIB~2017. Our pipeline (i) recovers compact, reusable model structure from flat solver formulations, (ii) reverse-generates natural-language specifications explicitly tied to this recovered structure under a unified model--data separation format, and (iii) performs iterative semantic validation through expert review and human--LLM interaction with independent reconstruction checks. This yields 223 one-to-one reconstructions that preserve the mathematical content of the original instances while enabling realistic natural-language-to-optimization evaluation. Experiments show substantial performance degradation on MIPLIB-NL for systems that perform strongly on existing benchmarks, exposing failure modes invisible at toy scale.
Distributed dynamic modeling and monitoring for large-scale industrial processes under closed-loop control
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University（工业控制技术国家重点实验室，控制科学与工程学院，浙江大学）；Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems（复杂系统先进控制与智能自动化湖北省重点实验室）；Department of Chemical and Materials Engineering, University of Alberta（阿尔伯塔大学化学与材料工程系）
VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）；State Key Laboratory of Intelligent Manufacturing System Technology（智能制造系统技术国家重点实验室）；School of Computer Science and Artificial Intelligence, Zhengzhou University（郑州大学计算机科学与人工智能学院）
A Unified Framework to Classify Business Activities into International Standard Industrial Classification through Large Language Models for Circular Economy
A Hybrid Approach of Transfer Learning and Physics-Informed Modeling: Improving Dissolved Oxygen Concentration Prediction in an Industrial Wastewater Treatment Plant
Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes
Artificial Intelligence Research Center（人工智能研究中心）；Knowledge Information Processing Laboratory（知识信息处理实验室）；Fujitsu Laboratories Ltd.（Fujitsu实验室有限公司）；Japan（日本）
Implementing NLPs in industrial process modeling: Addressing Categorical Variables
University of Luxembourg（卢森堡大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；CERATIZIT Luxembourg S.à r.l.（赛瑞特卢森堡有限公司）；CERATIZIT Austria GmbH（赛瑞特奥地利有限公司）；National Technical University of Athens（雅典国家技术大学）
Machine learning for industrial sensing and control: A survey and practical perspective
University of British Columbia（不列颠哥伦比亚大学）；University of Alberta（阿尔伯塔大学）；Incheon National University（仁川国立大学）；Central South University（中南大学）；Imperial College London（伦敦帝国理工学院）；Seoul National University（首尔国立大学）
